

import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= filter(lambda a__: p.requires_grad  ,	model.parameters())
			a_						:						Optional[int] 							= sum([np.prod(p.size()) for p in model_parameters])
			return params


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			if metric == "rouge2":
						a_						:						Union[str, Any] 							= """{val_avg_rouge2:.4f}-{step_count}"""
			elif metric == "bleu":
						a_						:						Dict 							= """{val_avg_bleu:.4f}-{step_count}"""
			elif metric == "em":
						a_						:						List[Any] 							= """{val_avg_em:.4f}-{step_count}"""
			elif metric == "loss":
						a_						:						int 							= """{val_avg_loss:.4f}-{step_count}"""
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    """ function.""")

			a_						:						Dict 							= ModelCheckpoint(
			    dirpath=a__  ,	filename=a__  ,	monitor=f'''val_{metric}'''  ,	mode="""max"""  ,	save_top_k=1  ,	every_n_epochs=1  ,	)

			return checkpoint_callback


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			return EarlyStopping(
			    monitor=f'''val_{metric}'''  ,	mode="""min""" if """loss""" in metric else """max"""  ,	patience=a__  ,	verbose=a__  ,	)



class        A__(pl.Callback							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Optional[int]:
									a_						:						List[Any] 							= {F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups    )}
									pl_module.logger.log_metrics(_lowercase    )





						@rank_zero_only
						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=True    )     -> None:
									logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''    )
									a_						:						List[str] 							= trainer.callback_metrics
									trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}    )
									# Log results
									a_						:						Optional[Any] 							= Path(pl_module.hparams.output_dir    )
									if type_path == "test":
												a_						:						Union[str, Any] 							= od / """test_results.txt"""
												a_						:						Tuple 							= od / """test_generations.txt"""
									else:
												# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
												# If people want this it will be easy enough to add back.
												a_						:						Optional[Any] 							= od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
												a_						:						Optional[Any] 							= od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
												results_file.parent.mkdir(exist_ok=_lowercase    )
												generations_file.parent.mkdir(exist_ok=_lowercase    )
									with open(_lowercase  ,      """a+"""    ) as writer:
												for key in sorted(_lowercase    ):
															if key in ["log", "progress_bar", "preds"]:
																		continue
															a_						:						Optional[Any] 							= metrics[key]
															if isinstance(_lowercase  ,      torch.Tensor    ):
																		a_						:						str 							= val.item()
															a_						:						Optional[int] 							= F'''{key}: {val:.6f}\n'''
															writer.write(_lowercase    )

									if not save_generations:
												return

									if "preds" in metrics:
												a_						:						Union[str, Any] 							= """\n""".join(metrics["""preds"""]    )
												generations_file.open("""w+"""    ).write(_lowercase    )





						@rank_zero_only
						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> Tuple:
									try:
												a_						:						Any 							= pl_module.model.model.num_parameters()
									except AttributeError:
												a_						:						List[str] 							= pl_module.model.num_parameters()

									a_						:						int 							= count_trainable_parameters(_lowercase    )
									# mp stands for million parameters
									trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}    )





						@rank_zero_only
						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> List[str]:
									save_json(pl_module.metrics  ,      pl_module.metrics_save_path    )
									return self._write_logs(_lowercase  ,      _lowercase  ,      """test"""    )





						@rank_zero_only
						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase    )     -> str:
									save_json(pl_module.metrics  ,      pl_module.metrics_save_path    )
									# Uncommenting this will save val generations
									# return self._write_logs(trainer, pl_module, "valid")





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


